Baserow VS superset

4. Baserow - 1.5k ⭐️

GitLab repository: https://gitlab.com/bramw/baserow.

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Check out the full roundup: https://baserow.io/blog/1-14-release-of-baserow. Test out Baserow 1.14: https://baserow.io. GitLab repository: Bram Wiepjes / baserow · GitLab.

Baserow

I'd also written an article on token authentication for django: https://www.spapas.net/2021/08/25/django-token-rest-auth/ using the REST Framework's TokenAuthentication.

This is simplest thing for most cases.

The session authentication that is proposed in the article is also great but has two problems:

* It will be hacky to implement for mobile apps (it should be possible but would not be something I'd like to do, I had tried in the past and remember that I needed to jump to a lot of hoops to "pick" that session cookie)

* The cookies can't be shared between different domains (cookies be shared the same domain or between a parent and child domain, i.e api.example.com can set/get cookies from .example.com).

So you can use the SessionAuthnentication if your frontend and backend share their domain and you know that your API won't ever be used for mobiles apps. On all other cases use TokenAuthentication.

I don't have experience with JWT Authentication, however I know it can be done and is used be various apps f.e baserow: https://gitlab.com/bramw/baserow/-/blob/develop/backend/src/...

Feel free to dig into https://gitlab.com/bramw/baserow repository, e.g. https://gitlab.com/bramw/baserow/-/blob/develop/backend/Dockerfile... There are docker compose files too.

Great idea! I've created an issue for it on the backlog https://gitlab.com/bramw/baserow/-/issues/1399.

Here is the full scoop on all new things: https://gitlab.com/bramw/baserow/-/releases/1.13.1.

By making RisingWave compatible with PostgreSQL, we ensured that any developer familiar with SQL could immediately start writing streaming queries. This wasn't just about syntax; it meant RisingWave could plug seamlessly into existing data workflows and connect easily with a vast ecosystem of familiar tools like DBeaver, Grafana, Apache Superset, dbt, and countless others.

First, cloned the Apache superset repository.

Open source tools like Apache Superset, Airbyte, and DuckDB are providing cost-effective and customizable solutions for data professionals. Becoming adept at these tools not only reduces dependency on proprietary software but also fosters community engagement.

ClickHouse is highly compatible with a wide range of data tools, including ETL/ELT processes and BI tools like Apache Superset. It supports virtually all common data formats, making integration seamless across diverse ecosystems.

With the transition from ETL to ELT, data warehouses have ascended to the role of data custodians, centralizing customer data collected from fragmented systems. This pivotal shift has been enabled by a suite of powerful tools: Fivetran and Airbyte streamline the extraction and loading, DBT handles the transformation, and robust warehousing solutions like Snowflake and Redshift store the data. While traditionally these technologies catered to analytical and business intelligence applications (think Looker and Superset), there's an increasing recognition of their potential for more dynamic operational analytics, delivering real-time data for actionable insights.

git clone --branch 4.0.2 --depth 1 https://github.com/apache/superset.git cd superset sudo docker compose up

Also, instead of the custom Dashboard app, a proper BI tool like Power BI, Tableau, Apache Superset, ..., etc. will be more powerful and flexible.